Robust Estimation of Background for Fixed Cameras

This paper proposes a robust background estimator for fixed cameras, to be used for foreground segmentation in tracking systems. The estimator is based on a variation of Stauffer's dynamic background algorithm, where the background learning rate is spatiotemporally adapted. The adaptation is based on the position, size and velocity of the various foreground objects already detected. The evidence for the initialization and tracking of the foreground objects is obtained by combining a pixel map showing the temporal persistence of each image pixel and the edge binary image. The spatiotemporal adaptation of the learning rate overcomes the problem of fading immobile or slowly moving objects into the background encountered in all to-date variations of Stauffer's algorithm, while the combination with edge information allows for objects already present in the scene at startup time and new objects to be treated by the same image processing module